Invariant-based dimensional reduction of object recognition features, systems and methods

ABSTRACT

A sensor data processing system and method is described. Contemplated systems and methods derive a first recognition trait of an object from a first data set that represents the object in a first environmental state. A second recognition trait of the object is then derived from a second data set that represents the object in a second environmental state. The sensor data processing systems and methods then identifies a mapping of elements of the first and second recognition traits in a new representation space. The mapping of elements satisfies a variance criterion for corresponding elements, which allows the mapping to be used for object recognition. The sensor data processing systems and methods described herein provide new object recognition techniques that are computationally efficient and can be performed in real-time by the mobile phone technology that is currently available.

This application claims priority to U.S. provisional application61/941,989, filed Feb. 19, 2014. U.S. provisional application 61/941,989and all other extrinsic references contained herein are incorporated byreference in their entirety.

FIELD OF THE INVENTION

The field of the invention is object recognition technology.

BACKGROUND

The following description includes information that may be useful inunderstanding the present invention. It is not an admission that any ofthe information provided herein is prior art or relevant to thepresently claimed invention, or that any publication specifically orimplicitly referenced is prior art.

Consumers continue to experience an increasingly blurred distinctionbetween real-world and on-line interactions. With the advent of objectrecognition technologies available today, consumers can now virtuallyinteract with real-world objects through their smart phones and othermobile electronic devices. For example, consumers can capture an imageof a movie poster via their cell phones. In response, the cell phone canconstruct an augmented reality interaction or game overlaid on thedisplay of the cell phone. In fact, the Applicants have pioneered suchtechnologies through their iD® technologies as implemented by DreamPlay™(see URLwww.polygon.com/2013/1/9/3851974/disney-dreamplay-ar-app-disney-infinity)Other technologies that attempt to offer similar experiences include thefollowing:

-   -   Layar® (see URL www.layar.com),    -   BlippAR.com™ (see URL www.blippar.com), and    -   13th Lab (see URL www.13thlab.com).

Unfortunately, such technologies are limited in scope and typically areonly capable of recognizing a single object at a time (e.g., a singletoy, a single person, a single graphic image, etc.). In addition, aconsumer must position their cell phone into a correct position ororientation with respect to the object of interest, then wait for theirthe cell phone to analyze the image information before engaging contentis retrieved. Ideally a consumer should be able to engage contentassociated with an object of interest very quickly and should be able toengage many objects at the same time. The above referenced companiesfail to provide such features.

Objects represented in image data can be recognized through descriptorsderived from the image data. Example descriptors include those generatedfrom algorithms such as SIFT, FAST, DAISY, or other patternidentification algorithms. Some descriptors can be considered torepresent a multi-dimensional data object, a vector or a histogram forexample. However, the dimensions of the descriptor do not necessarilyhave equivalent object discriminating capabilities. Principle ComponentAnalysis (PCA) can provide for statistical identification of whichdescriptor dimensions are most important for representing a training setof data. Unfortunately, PCA fails to provide insight into thediscriminative power of each dimension or identifying which dimension ofthe descriptor would have greater discriminating power with respect toan environmental parameter (e.g., lighting, focal length, depth offield, etc.). As such, each dimension has to be processed in everyinstance to determine discriminating features.

U.S. Pat. No. 5,734,796 “Self-Organization of Pattern Data WithDimensional Reduction Through Learning of Non-LinearVariance-Constrained Mapping” issued to Pao, filed Sep. 29, 1995,provides systems and methods for visualizing a large body ofmulti-featured pattern data (e.g., chemical characteristic information)in a computationally efficient manner. The process involves subjectingthe multi-featured pattern data to a nonlinear mapping from the originalrepresentation to one of reduced dimensions using a multilayerfeed-forward neural net. While advantageous in some regards, Pao failsto appreciate that data can be acquired in a controlled environmentunder different conditions to empirically identify dimensions that canbe reduced or ignored.

U.S. Pat. No. 6,343,267 “Dimensionality Reduction For SpeakerNormalization and Speaker and Environment Adaptation Using EigenvoiceTechniques” issued to Kuhn et al., filed Sep. 4, 1998, describestechniques for speaker normalization in the context of speechrecognition by an initially speaker-independent recognition system. Thetechnique enables the speaker-independent recognition system to quicklyreach a performance level of a speaker-dependent system withoutrequiring large amounts of training data. The technique includes aone-time computationally intensive step to analyze a large collection ofspeaker model data using dimensionality reduction. Thereafter, acomputationally inexpensive operation can be used for a new speaker toproduce an adaptation model for the new speaker. Like Pao, Kuhn fails toappreciate that data can be acquired in a controlled environment underdifferent conditions to empirically identify dimensions that can beignored.

Some references contemplate controlling a data acquisition environmentwithin the context of imaging and image analysis. For example, U.S. Pat.No. 7,418,121 “Medical Image Processing Apparatus and Medical ImageProcessing System” issued to Kasai, filed Dec. 10, 2004, describes amedical diagnostic imaging processing system that updates its trainingdata by customizing a detection condition. The purpose of updating thetraining data is to enhance the system's diagnostic capabilities withina specialized medical field. Kasai fails to describe modifying adetection condition to empirically identify dimensions within a data setthat can be ignored to improve computational efficiency for imageprocessing.

U.S. Pat. No. 8,565,513 “Image Processing Method For Providing DepthInformation and Image Processing System Using the Same” issued to Shaoet al., filed Dec. 8, 2009, describes a method of estimating a depth ofa scene or object in a 2D image by capturing different view angles ofscene or object. Shao fails to appreciate that different views of theobject can be used to empirically identify image descriptors that areless relevant for image recognition processing.

In the publication “Actionable Information in Vision” by Soatto,published in Proceedings of the International Conference on ComputerVision, October 2009, (see URL vision.ucla.edu/publications.html),Soatto states that the data acquisition process can be controlled (whichhe refers to as “Controlled Sensing”) to counteract the effect ofnuisances. Soatto fails to discuss controlling the parameters and/orattributes of a data acquisition environment for the purposes ofempirically identifying dimensions that can be reduced (e.g., ignored).

Object recognition techniques can be computationally expensive. Theenvironments in which object recognition can be of most use to a user isoften one in which the devices available for object capture andrecognition have limited resources. Mobile devices, for example, oftenlack the computational capabilities of larger computers or servers, andnetwork capabilities is often not fast enough to provide a suitablesubstitute. Thus, processing every dimension for discrimination witheach execution of an object recognition technique can cause latency inexecution, especially with multiple objects and/or in computationallyweak computing devices. For certain applications, such as augmentedreality gaming applications, this latency can render the applicationunusable. None of the references mentioned above provides an accurateand computationally inexpensive object recognition technique thatinvolves empirically identifying dimensions that can be ignored. Thus,there is still a need to improve upon conventional object recognitiontechniques.

All publications herein are incorporated by reference to the same extentas if each individual publication or patent application werespecifically and individually indicated to be incorporated by reference.Where a definition or use of a term in an incorporated reference isinconsistent or contrary to the definition of that term provided herein,the definition of that term provided herein applies and the definitionof that term in the reference does not apply.

SUMMARY OF THE INVENTION

The inventive subject matter provides apparatus, systems and methods inwhich a sensor data processing system provides object recognitioncapabilities without the need for intensive computations. The sensordata processing system includes a controlled sensing environment thathas configurable environmental parameters (e.g., lighting, orientationof object, camera settings, focal length, resolution, depth of field,etc.). Each environmental parameter has one or more configurableattributes. For example, if the environmental parameter is lighting, thecorresponding attribute(s) could be 100 lux, 2 meters from light source,120 watt incandescent bulb, etc. The controlled sensing environment isavailable at object training time, but is not a requisite for the sensordata processing system to recognize objects when not training.

The sensor data processing system also includes an image processingengine. In one aspect of some embodiments, the image processing engineincludes a processor that is functionally coupled with a non-transitoryelectronic storage medium and capable of executing a set of executableinstructions (e.g., software code) stored therein. The executableinstructions are configured to analyze sensor data (e.g., image signal)and recognize an object in the sensor data in a manner that requiresminimal computational resources compared to known object recognitionmethods and systems. In another aspect of some embodiments, theexecutable instructions can be additionally configured to recognizeobjects in the sensor data in a scale invariant manner.

In further aspects of some embodiments, the executable instructions canbe organized into modules that execute different steps or functions. Insuch embodiments, the image processing engine can include a traitrecognition module that obtains a first training data set from a sensor.For example, the trait recognition module could receive a first imagesignal from a camera. The first training data set is representative ofat least one object under a defined environmental state within thecontrolled environment. The trait recognition module is configured toderive a first recognition trait from the first training data setaccording to a trait extraction algorithm. The first recognition traitis representative of a plurality of elements that describe features orproperties of the object.

Once the first recognition trait is derived, one or more of theenvironmental parameters and its corresponding environmental attributesin the controlled imaging environment can be adjusted or modified tocreate a new environmental state (e.g., a second environmental state).The adjustment can occur manually by a user or automatically by anenvironmental configuration module of the image processing engine.

The trait recognition module is configured to obtain a second trainingdata set representative of the object under the new environmental statewithin the controlled environment. Using the second training data set,the trait recognition module can derive a new recognition traitaccording to the trait extraction algorithm. The new recognition traitis representative of a second plurality of elements that describefeatures or properties of the object in the new environmental state.Trait recognition module is also configured to derive or otherwisedetermine a correspondence between the first trait and the new trait.

The image processing engine also has a mapping module that is configuredto identify a mapping that maps the elements of first recognition traitand the elements of the new recognition traits to a new representationspace. The mapping identified by the mapping module satisfies traitelement variance criteria among corresponding elements in the traitsacross the first and second training set. The mapping module can befurther configured to store the mapping into a memory.

The methods and processes of the inventive subject matter can beemployed to reduce the computational load associated with objectrecognition, and as such improve the quality and speed of therecognition itself without having to increase computing capacity, whichin turn increases the viability of object recognition for applicationsin computationally-limited environments or that require minimal latencyto properly function.

Various objects, features, aspects and advantages of the inventivesubject matter will become more apparent from the following detaileddescription of preferred embodiments, along with the accompanyingdrawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a schematic of a sensor data processing system.

FIG. 1B is a schematic of the sensor data processing system of FIG. 1Aillustrating multiple environmental states.

FIG. 2 is an image of an object in a first environmental state.

FIG. 3A is the image of FIG. 2 with recognition traits highlighted.

FIG. 3B is the image of FIG. 3A, with a first recognition traithighlighted.

FIG. 4 is an image of the object of FIG. 2 in a second environmentalstate.

FIG. 5 is the image of FIG. 4 with a second recognition traithighlighted.

FIG. 6 is a schematic of another embodiment of a sensor data processingsystem.

DETAILED DESCRIPTION

Throughout the following discussion, numerous references will be maderegarding servers, services, interfaces, engines, modules, clients,peers, portals, platforms, or other systems formed from computingdevices. It should be appreciated that the use of such terms is deemedto represent one or more computing devices having at least one processor(e.g., ASIC, FPGA, DSP, x86, ARM, ColdFire, GPU, multi-core processors,etc.) configured to execute software instructions stored on a computerreadable tangible, non-transitory medium (e.g., hard drive, solid statedrive, RAM, flash, ROM, etc.). For example, a server can include one ormore computers operating as a web server, database server, or other typeof computer server in a manner to fulfill described roles,responsibilities, or functions. One should further appreciate thedisclosed computer-based algorithms, processes, methods, or other typesof instruction sets can be embodied as a computer program productcomprising a non-transitory, tangible computer readable media storingthe instructions that cause a processor to execute the disclosed steps.The various servers, systems, databases, or interfaces can exchange datausing standardized protocols or algorithms, possibly based on HTTP,HTTPS, AES, public-private key exchanges, web service APIs, knownfinancial transaction protocols, or other electronic informationexchanging methods. Data exchanges can be conducted over apacket-switched network, the Internet, LAN, WAN, VPN, or other type ofpacket switched network. One should appreciate that the presentinventive subject matter provides numerous technical effects, such assystems and methods for three-dimensional object recognition.

As used herein, references to computing devices, modules, engines,processors and/or any other components being “configured to” and“programmed to” execute instructions to carry out steps and functions ofthe inventive subject matter are used interchangeably, and are intendedto refer to one or more computing devices, processors, engines, modulesor other hardware computing components having instructions loaded forexecution.

The following discussion provides many example embodiments of theinventive subject matter. Although each embodiment represents a singlecombination of inventive elements, the inventive subject matter isconsidered to include all possible combinations of the disclosedelements. Thus if one embodiment comprises elements A, B, and C, and asecond embodiment comprises elements B and D, then the inventive subjectmatter is also considered to include other remaining combinations of A,B, C, or D, even if not explicitly disclosed.

FIG. 1A shows an overview of sensor data processing system 100. System100 includes an image processing engine 150 communicatively coupled withone or more sensors 108, and programmed to receive sensor data 130 fromone or more sensors 108 within a controlled sensing environment 110.

Controlled sensing environment 110 can be considered to be theenvironment within which the sensor 108 can sense, detect, or otherwiseperceive an object 101. Controlled sensing environment 110 includes aplurality of controllable or adjustable environmental parameters 103. Atleast some of the environmental parameters 103 are capable of affectinghow an object 101 within environment 110 is perceived or otherwisesensed by sensor(s) 108. Each environmental parameter 103 can have oneor more environmental attribute 104 that further defines (provides avalue for) the respective parameter.

Examples of environmental parameters 103 and corresponding attributes104 include lighting properties such as type of light temperature (e.g.,Kelvin measurements), light source (e.g., indoor lighting, outdoorlighting, incandescent, fluorescent, bulb wattage, etc), light intensity(e.g., lux measurements), lighting position with respect to object(e.g., above object, in front of object, etc.), light source distancefrom object, and light type (e.g., natural, window, flash, ambient,combinations thereof, light color, etc.). It is further contemplatedthat environmental parameters 103 can include time (e.g., a duration orlength of time, sampling or analysis frequency, distortions of time viaslowing down or speeding up sensor data playback, etc.). This allows foranalysis of objects within an environment that are dynamic and changewith time, such as with medical imaging of a heart beating.

Other examples of environmental parameters 103 and attributes 104 mayinclude sensor modalities and/or sensor properties of sensor 108. Forexample, sensor 108 could comprise an image sensor (e.g., CCD sensordigital camera, CMOS sensor digital camera, thermal imager or infraredcamera), audio transducer (e.g., microphone, acoustic resonant masssensor, acoustic response measurements, acoustic properties, etc.),olfactory sensor, or any other sensor capable of producing unique datasets suitable for identifying an object. Because environmentalparameters 103 and attribute 104 can include sensor modalities and/orproperties, the controlled sensing environment 110 is not limited to thephysical environment within which the object 101 resides but alsoextends to include the controlled parameters of the sensor 108 and othernon-physical parameters that can affect the sensor 108.

When the sensor is an image sensor, such as a digital camera, examplesof environmental parameters 103 and attributes 104 may include camerasettings and properties such as aperture size (e.g.,ƒ/1.4,ƒ/2,ƒ/2.8,ƒ/4,ƒ/5.6,ƒ/8, etc.), shutter speed or exposure time(e.g., 1/2000 sec, 1/250 sec, etc.), film sensitivity (e.g., ISO 100,ISO 400, ISO 3200, etc.), flash settings, resolution settings (e.g., 1.3megapixel, 2.1 megapixel, 5.0 megapixel, etc.), video frame rate (e.g.,20 FPS, 30 FPS, 60 FPS, etc.), video aspect ratio (e.g., 4:3, 16:9,etc.), shooting mode (e.g. fully automatic, manual, aperture-priority,shutter-priority, etc.), camera type, and lens type.

When the sensor 108 is an audio sensor, environmental parameters caninclude sound volume, amplitude, sound absorption, pitch, duration,speech recognition parameters (e.g., patterns, tone, etc.), frequency,object movement direction, phase, etc.

Environmental parameters 103 and corresponding attributes 104 may alsoinclude data communication properties, such as communication type (e.g.,wired, wireless, fiber optic cable, Ethernet cable, etc.), communicationprotocol (Bluetooth®, Wi-Fi®, TCP/IP, etc.), and communication signalintegrity (e.g., signal strength, network latency, etc.). For wirelesssignals, environmental parameters 103 and their attributes 104 can alsoinclude aspects of the environment that might affect the transmission ofthe signals such as the size and number of objects within theenvironment (e.g., number of people in a room), shape of environment,materials present in the environment (including the materials of theobjects within the environment), reflectivity, absorption, etc.

Other examples of environmental parameters 103 and associated attributes104 can include weather properties (e.g., wind speed, humidity, %overcast, altitude, atmospheric pressure), gravity, magnetic fieldintensity, electrical field intensity, and other properties of aphysical environment. In addition, environmental parameters 103 andattributes 104 can include object properties of object 101, such asshape, distance from sensor 108, orientation with respect to the sensor108, field of view, surface reflectivity, texture, surface roughness,object color, object size, object opacity, object transparency, objecttranslucency, heat emission, noise emission (e.g., volume, duration,etc.), or any other measureable attribute of the object. Environmentalparameters 103 and attributes 104 can further include system-dependenterror rates and error rates of trait extraction algorithms.

As can be appreciated by the astute reader, there are certainenvironmental parameters that are modality-specific and others that canaffect sensors 108 of multiple types. As such, certain environmentalparameters can be correlated with sensor data modality for adjustment(especially in multi-sensor environments covering a plurality ofmodalities). For example, lighting intensity will not affect an audiosensor. Similarly, unless of a magnitude to affect a camera viavibration, audio volume will not affect sensor data captured by an imagesensor. However, an object orientation for an object that outputs audio(such as a speaker) within an environment can affect the perception ofthe object in both the visual (i.e., the object looks different becauseof its changed orientation relative to a camera) and audio (i.e., thesound is projected in a different direction and can affect how amicrophone detects it, such as if the sound goes from being directedtowards a microphone to being directed away from a microphone)modalities.

The examples of environmental parameters 103 and environmentalattributes 104 mentioned above are not an exhaustive list and are merelyprovided for exemplary purposes. As seen from the examples above,environmental parameters 103 (and their corresponding environmentalattributes 104) may include properties that are human sensible (e.g.,lighting) and human non-sensible (e.g., signal strength, infraredlighting).

At any given time, a controlled sensing environment 110 will have anenvironmental state 102. Environmental state 102 is defined at least inpart by environmental parameters 103 and environmental attributes 104.Environmental state 102 can be considered to be a state of anenvironment having at least some known, defined, or at least definable,environmental parameters 103 and attributes 104. Additionally, inembodiments, at least some of these known, defined or definableparameters 103 and/or attributes 104 can further be modified,controlled, adjusted or otherwise changed. In some instances,environmental state 102 can be representative of an expected environmentstate external to controlled sensing environment 110.

Object 101 is typically a physical real-world object having at leastsome characteristics or properties that can be sensed, detected orotherwise perceived by sensor 108. However, it is contemplated that theinventive subject matter can be applied to synthetic, computer-modeledenvironments. As such, the object 101 can be a virtual object within thecomputer-modeled environments. Within the modeled environments, sensors108 can be simulations of real-world sensors within the virtualenvironment, such that they capture sensor data of the (virtual) object101 in the same way a real-world corresponding sensor captures sensordata of the real-world environment being simulated. In embodiments, thesensors 108 can be real-world sensors capturing a virtual or simulatedenvironment (such as a camera capturing imagery of a virtual environmentwith a virtual object shown via a display device).

As shown in FIG. 1A, image processing engine 150 includes a sensor datacapturing module 151, a trait recognition module 152 and a mappingmodule 160. The image processing engine 150 can be embodied ascomputer-executable instructions stored on one or more non-transitorycomputer-readable memory that are executed by one or more processors toexecute the functions and processes of the inventive subject matter. Inembodiments, the image processing engine 150 can be one or more hardwareprocessors specifically hard-coded to execute the functions andprocesses of the inventive subject matter.

The sensor data capturing module 151 is configured to obtain orotherwise receive sensor data 130 from one or more sensors 108. Ingeneral, sensor data set 130 is representative of an object 101 underthe environmental conditions (i.e., the environmental state 102) withinthe controlled sensing environment 110 as perceived by sensor 108.Examples of sensor data sets 130 can include image data, video data,audio data, temperature data, barometric pressure data, wind speed data,humidity data, pressure sensor data, accelerometer data, biometrics, andany other type of sensor data generated by sensors (including sensordata corresponding to the example sensors mentioned herein).

After module 151 obtains sensor data set 130, trait recognition module152 uses the sensor data set 130 to derive a recognition trait 154according to a trait extraction algorithm. The trait extractionalgorithm used by trait recognition module 152 can incorporate one ormore classification algorithm, object recognition algorithm, and/orimage processing algorithm. Examples of trait extraction algorithmsinclude, but are not limited to: scale-invariant feature transform(SIFT), features from accelerated segment test (FAST), gradientlocation-orientation histogram (GLOH), (DAISY), binary robustindependent elementary feature (BRIEF), oriented fast and rotated BRIEF(ORB), binary robust invariant scalable keypoints (BRISK), fast retinakeypoint (FREAK), speed-up robust feature (SURF), visual simultaneouslocalization and mapping (vSLAM), simultaneous localization and mapping(SLAM), and based upon related sequence types (BURST). In someembodiments, the trait extraction algorithm may comprise an automaticspeech recognition algorithm. Other suitable trait extraction algorithmsand techniques include edge-based recognition techniques such as thosedescribed in Applicant's U.S. provisional application No. 61/989,445titled “Image-Based Feature Detection Using Edge Vectors” andApplicant's U.S. patent application Ser. No. 14/623,341 titled“Edge-Based Recognition, Systems and Methods”, both of which areincorporated by reference herein in their entirety.

Recognition trait 154 can be made up of one or more elements (e.g.,sub-units of a trait). For example, in some embodiments, a recognitiontrait 154 can be an image descriptor such as a SIFT descriptor. The SIFTdescriptor could have 128 dimensions, each dimension having a value. Theone or more elements of recognition trait 154 could represent the valuesof the 128 dimensions of the SIFT descriptor.

In another aspect of some embodiments, a plurality of recognition traitscan be a cluster or clusters of image descriptors (e.g., groups ofdescriptors that share some common feature or association). In somecases, the cluster(s) may represent a constellation(s) of descriptorswithin a descriptor space (e.g., 2D space, 3D space). In yet anotheraspect of some embodiments, the one or more elements may represent adescriptor location that indicates the location of a descriptor withrespect to other descriptors within a descriptor space. The descriptorlocation could also be the location of a cluster of descriptors (e.g.,for bag-of-features classification model). Descriptor clusters can beused to generate a trait vocabulary. The trait vocabulary is discussedin further detail below.

As discussed above in reference to FIG. 1A, the system 100 includes acontrolled sensing environment 110 whose environmental state 102 isdefined by the environmental parameters 103 and their correspondingenvironmental attributes 104. Thus, changes to the environmentalparameters 103 and/or attributes 104 (e.g. the values) can be consideredto define new environmental states 102. FIG. 1B provides an overview ofthe system 100 of FIG. 1A, illustrating two different environmentalstates 102 a and 102 b within controlled sensing environment 110 duringthe execution of functions and processes of the inventive subjectmatter.

As shown in FIG. 1B, the first and second environmental states 102 a and102 b each include respective environmental parameters 103 a and 103 bwith their respective corresponding environmental attributes 104 a and104 b.

Environmental parameters 103 a and 103 b will generally be the same setof parameters, or at least include at least one common environmentalparameter whose change can be monitored and/or controlled within thecontrolled sensing environment 110. Thus, the differences betweenenvironmental state 102 a and 102 b will be differences in one or moreenvironmental attributes 104 a and 104 b for environmental parameters103 a, 103 b that are common to both environmental states 102 a, 102 b.

FIG. 1B also illustrates objects 101 a and 101 b within environmentalstates 102 a and 102 b, respectively. Both objects 101 a and 101 b arethe same physical object 101 from FIG. 1A. As the object 101 exists indifferent environmental conditions/states (and possible differenttemporal states), and as such, are perceived differently by sensor 108.In other words, object 101 a can be considered to be the perception ofthe object 101 by sensor 108 under the environmental conditions ofenvironmental state 102 a and object 101 b can be considered to be theperception of the object 101 by sensor 108 under the (different)environmental conditions of environmental state 102 b.

As illustrated in FIG. 1B, sensor data capturing module 151 firstreceives a set of sensory data 130, referred to as first training dataset 130 a. FIG. 2 illustrates one example of a first training data set130 a, namely, image 200. Image 200 is a photograph of an object 101 ain an environmental state 102 a. In this particular example, object 101is a 3D toy figurine and object 101 a is the 3D toy figurine 101captured by the sensor 108 as shown in FIG. 2. As discussed, above, anenvironmental state 102 can be defined by many different parameters 103and corresponding attributes 104, such as the lighting, backgroundcolors, background objects, orientation of an object 101 with respect tothe camera 108, and so forth. For this illustrative example, theenvironmental parameters 103 a (and associated environmental attributes104 a) of controlled sensing environment 110 for environmental state 102a are considered to be the lighting conditions (e.g., brightness) andthe orientation of the figurine 101 a relative to camera 108.

After module 151 obtains data set 130 a, trait recognition module 152uses data set 130 a to derive a first recognition trait 154 a accordingto a trait extraction algorithm, as discussed above. FIG. 3A illustratesimage 200 having a plurality of recognition traits 154 identified on thefigurine 101 a by a trait extraction algorithm. In the example of FIG.3A, the trait extraction algorithm used by trait extraction module 152is SIFT. As such, the recognition traits 154 shown are SIFT keypoints(represented by a circle with a radial line indicating an orientation).One or more of these recognition traits 154 can be selected by the imageprocessing engine 150 as first recognition trait(s) 154 a. The selectionof recognition traits as first recognition trait(s) 154 a for use inprocessing can depend on the size of the descriptor, the orientation, orother characteristics of the recognition trait. In embodiments, thefirst recognition trait 154 a can be user-selected, such as by clickingor otherwise highlighting the desired recognition trait 154 within theimage 200. However, more interesting embodiments automatically processthe traits.

FIG. 3B is a highlighted example of a first recognition trait 154 a,which comprises one or more descriptive elements (e.g., descriptors)that fall on feature 158 a (e.g., a rope belt on the figurine) of object101 a that have been derived from the trait extraction algorithm. Withinthe image 200, the keypoint of first recognition trait 154 a is locatedat an (x, y) pixel location coordinate of the image. For this example,the keypoint of first recognition trait 154 a is considered to belocated at pixel location (376, 729) of image 200.

A descriptor as used in this example can be represented as a vector,which has a concrete, defined number of dimensions having acorresponding value (even if the value is zero). In the example of FIG.3B, the descriptors used are a 128-dimension SIFT descriptors. As such,the descriptor of first recognition trait 154 a resulting from the traitextraction module is considered to be {0, 0, 6, 24, 13, 33, 5, 0, 4, 7,58, 81, 45, 128, 34, 17, 140, 67, 43, 4, 1, 6, 20, 140, 64, 14, 1, 0, 0,0, 0, 41, 0, 0, 0, 1, 26, 40, 4, 0, 16, 6, 2, 7, 140, 140, 29, 13, 140,27, 2, 3, 20, 10, 17, 93, 63, 14, 2, 0, 0, 0, 0, 21, 0, 0, 0, 10, 21, 3,0, 0, 19, 1, 1, 25, 140, 51, 1, 7, 140, 14, 1, 1, 38, 17, 1, 50, 63, 9,1, 0, 0, 0, 0, 21, 0, 0, 1, 3, 7, 7, 0, 0, 13, 4, 3, 17, 140, 42, 1, 2,140, 55, 11, 3, 39, 6, 2, 22, 27, 20, 29, 0, 0, 0, 0, 6}.

Returning to FIG. 1B, once module 152 has derived the first recognitiontrait 154 a, the second environmental state 102 b is created byadjusting one or more of the environmental parameters 103 andenvironmental attributes 104 of controlled sensing environment 110.Second environmental state 102 b is defined by environmental parameters103 b and corresponding environmental attributes 104 b, at least one ofwhich is different from one or more of the environmental parameters 103a and/or one or more of the corresponding environmental attributes 103of environmental state 102 a. In this illustrative example, theenvironmental parameters 103 b remain the same as the environmentalparameters 103 a, and the environmental attributes 104 b correspondingto lightning and orientation of the figurine 101 have been changed fromthe environmental attributes 104 a. In other words, environmental state102 a differs from environmental state 102 b in that the environmentalattributes of the lighting environmental parameter 103 within thecontrolled sensing environment 110 and orientation environmentalparameter 103 of object 101 relative to the camera 108 have beenchanged.

The sensor data capturing module 151 then obtains or otherwise receivesa second training data set 130 b from sensor 108. The second trainingdata set 130 b is representative of object 101 b in the secondenvironmental state 102 b. FIG. 4 illustrates one example of a secondtraining data set 130 b, namely, image 400. Image 400 is a photograph ofan object 101 b in an environmental state 102 b. Object 101 b is thesame 3D toy figurine 101, which is depicted as object 101 a in FIG. 2.However, object 101 b is perceived by the camera 108 in a slightlydifferent manner given that object 101 b is in a different environmentalstate 102 b. As can be seen in FIG. 4, environmental state 102 b isdifferent than state 102 a in that the lighting condition is darker andthe orientation of the figurine 101 has been rotated counterclockwiseabout 45 degrees.

Using training data set 130 b, trait recognition module 152 derives asecond recognition trait 154 b. In this particular instance, the secondrecognition trait 154 b is one or more descriptors that fall on afeature 158 b of object 101 b, which have been derived using a traitextraction algorithm. Feature 158 b corresponds to the same physicalpart of object 101 as feature 158 a of FIG. 3B (the same part of therope belt of the figurine).

Second recognition trait 154 b has a correspondence to first recognitiontrait 154 a in some manner, such as a physical or logical geometry. Forexample, since image 200 and image 400 are obtained in a controlledsetting (e.g., controlled sensing environment 110), module 152 candetermine the (x, y) coordinate for each trait 154 a, 154 b and assign acorrespondence between traits having similar (x, y) coordinates, eventhough the two corresponding traits may have vastly different values.System 100 can use this correspondence to analyze for invariance amongtraits and/or trait elements by establishing commonalities or bases ofreference between the different training data sets 130 a and 130 b.

Another example of correspondences can include a correspondence betweentraits located at the same physical location of object 101 across bothenvironmental states 102 a, 102 b. If there are no changes in therelative orientation of the object 101 and sensor 108 (including nochanges in zooming or other spatial distortion features of sensor 108),the traits 154 a and 154 b corresponding to a particular physicalfeature of object 101 will be in identical (or very similar) (x, y)pixel locations within two images corresponding to training sets 130 a,130 b. If the relative orientation of the object 101 and sensor 108changes between environmental states 102 a and 102 b, image recognitiontechniques can be used to track the movement of a particular physicalfeature of object 101 between the images. Examples of suitable imagerecognition techniques that can be used to correlate or track a physicalfeature between two different images include the techniques disclosed inApplicant's U.S. patent application Ser. No. 13/330,883 titled “DepthEstimation Determination, Systems and Methods”, which is incorporated byreference herein in its entirety. It should be noted that, in certaincontrolled environments 110, models of object 101 can be generated andknown ahead of time such that the trait recognition module 152 canemploy the models to mirror the changes in the orientation of the object101 relative to the sensor 108 and thus track the location of the samephysical feature of the object 101 across multiple environmental states.

Other contemplated types of correspondences can include acousticcorrespondences (e.g., such as to identify a same part of a spoken wordspoken at different times), scaling factor correspondences, modalitiesof sensor data correspondences (e.g., modalities of image data such asinfrared image, RGB image, etc.)

In embodiments, a correspondence between recognition traits can be oneor more environmental parameters 103 and their attributes 104 thatremain unchanged between two different environmental states.

FIG. 5 illustrates one example of a second recognition trait 154 b,which comprises a plurality of descriptive elements (e.g., descriptors)of a portion of object 101 b (e.g., the rope belt on the figurine) thathave been derived from the trait extraction algorithm. Via imageprocessing techniques, the trait recognition module 152 can determinethe corresponding location within the image 400 of feature 128 b, whichis the physical feature of object 101 corresponding to feature 128 a ofimage 200 and use the corresponding keypoint and associated descriptorat that location as the second recognition trait 154 b. As such, in theillustrative example using figurine 101, the traits 154 a and 154 b havea correspondence of a same physical location on the figurine (eventhough their respective (x, y) locations within each of the images 200,400 are different and their descriptors are different).

The keypoint of second recognition trait 154 b is illustrated as acircle with a radial line indicating an orientation in FIG. 5. In thecurrent example, the keypoint of second recognition trait 154 b isconsidered to be located at pixel location (1503, 1552) of image 400. Aswith first recognition trait 154 a, second recognition trait 154 b is a128-dimension SIFT descriptor. In this example, the descriptor of secondrecognition trait 154 a resulting from the trait extraction module 152is considered to be {8, 0, 0, 0, 0, 1, 3, 49, 4, 0, 0, 0, 6, 14, 8, 24,28, 0, 0, 0, 2, 5, 16, 140, 8, 0, 0, 0, 14, 30, 42, 85, 8, 0, 1, 3, 3,2, 3, 36, 63, 3, 0, 7, 12, 6, 3, 24, 140, 13, 0, 0, 0, 3, 36, 140, 29,3, 0, 0, 36, 140, 100, 69, 2, 0, 3, 16, 4, 0, 2, 17, 54, 8, 1, 10, 4, 2,24, 44, 140, 140, 0, 0, 0, 3, 7, 22, 68, 140, 12, 9, 82, 70, 9, 10, 6,0, 0, 4, 3, 1, 3, 66, 4, 1, 0, 0, 4, 29, 43, 81, 20, 139, 5, 1, 17, 40,8, 2, 9, 140, 87, 25, 18, 2, 0, 0}.

It should be noted that the keypoints and descriptors illustrated withinand associated with FIGS. 3B and 5 are representative examples toillustrate their use according to aspects of the inventive subjectmatter. As such, the visual and data-value representations of thesekeypoints and descriptors are presented for clarity and are not intendedto represent exact values.

Mapping module 160 is configured to identify a mapping 162 that maps theelements of trait 154 a and trait 154 b to a new representation space.The mapping of the elements in the new representation space satisfiesvariance criteria among corresponding elements across the first trainingdata set 130 a and the second training data set 130 b. In someembodiments, the variance criterion is a function of a variance withrespect to one or more of the elements. The variance represents how“invariant” the elements are with respect to the adjusted environmentalparameter and attribute. For example, a (relatively) large variancemeans the element is likely not invariant, whereas a (relatively) smallvariance means that the element is empirically invariant.

Thus, to be able to identify a proper mapping 162 for traits 154 a and154 b the mapping module 160 first calculates a variance between thetrait 154 a and 154 b. The variance can be calculated for individualelements common to both the first recognition trait 154 a and the secondrecognition trait 154 b or for the recognition traits 154 a and 154 b asa whole (with aggregated scores or values for each trait calculated fromthe elements of the traits used to determine the variance).

Continuing with the illustrative example, the descriptors of firstrecognition trait 154 a and 154 b, the elements can be considered to bethe dimensions of descriptor. To find the variance, the mapping module160 can compare each dimension (or a pre-defined amount of dimensions atknown locations that are less than all of the dimensions) of the firstrecognition trait 154 a descriptor with a corresponding dimension of thesecond recognition trait 154 b descriptor.

For example, the first ten dimensions of the descriptor of trait 154 aare {0, 0, 6, 24, 13, 33, 5, 0, 4, 7, . . . } and the first tendimensions of the descriptor of trait 154 b are {8, 0, 0, 0, 0, 1, 3,49, 4, Thus, the mapping module 160 calculates the variance for thecorresponding first ten dimensions of the descriptors of trait 154 a and154 b as {8, 0, −6, −24, −13, −32, −2, 49, 0, −7, . . . }. It should benoted that the variance for each of the elements can be expressed interms of a positive or negative value (indicating a magnitude ofvariance and also direction) or, alternatively, simply be expressed asan absolute value of the variance (indicating a magnitude of varianceonly).

The module 160 can then determine whether one or more pairs ofcorresponding elements (in this example, descriptor dimensions) oftraits 154 a and 154 b can be considered to be invariant by applying atrait element invariance criteria to the variance. The trait elementinvariance criteria can be considered to be the criteria that defineswhether a particular trait element is invariant. For example, a traitelement invariance criteria can be a threshold variance value for aparticular element (or for a trait as a whole) such that a variance ofthe element from a first trait 154 a to a second trait 154 b that isgreater or equal to the threshold is considered variant. Conversely, avariance of the element of less than the threshold is consideredinvariant. As the variance represents a change in values, the thresholdcan comprise solely a magnitude of an element's change (e.g., theabsolute value of the variance is considered). The trait elementinvariance criteria can include threshold variance values that areuniform across all elements or that can differ for one or more of theelements in the traits.

Continuing with the example above, suppose that a trait elementinvariance criteria includes a variance threshold value of “10” for allof the elements (dimensions) of the traits (in this example, thedescriptors). For the variance of the corresponding elements of traits154 a and 154 b {8, 0, −6, −24, −13, −32, −2, 49, 0, −7, . . . }, withinthese first 10 dimensions, the mapping module 160 determines thatdimensions 1-3, 7, and 9-10 are invariant (because their respectivemagnitudes of 8, 0, 6, 2, 0, and 7 are less than the threshold value of10) and that dimensions 4-6 and 8 are variant (because their respectivemagnitudes of 24, 13, 32 and 49 are greater than the threshold value of10).

When module 160 determines that a pair of corresponding elements isinvariant, the pair of elements can be ignored (e.g., the data set ofelements can be dimensionally reduced) to thereby reduce thecomputational demand of module 160. Unlike dimensional reduction viaprinciple component analysis, which merely indicates from a statisticalperspective which dimensions have greatest or least variance, module 160empirically identifies or discovers invariant elements by adjusting oneor more environmental parameters in a controlled data acquisition (e.g.,data sensing) environment. Thus, the disclosed system is able todiscover empirical correlations between trait element variants (or lackof variations) with varied environmental properties. In some embodimentsonly a single environmental parameter 103 and/or attribute 104 isadjusted at a time to produce the second environmental state 102 b. Byadjusting one environmental attribute 104 at a time, module 160 canproduce a more sensitive mapping 162 due to smaller variances betweenthe corresponding elements of the recognition traits 154 a and 154 b.Controlled sensing environment 110 can be further configured to create athird, fourth, through n^(th) environmental state that has a third,fourth, through n^(th) training data set, each set providing additionalvariances under known environmental parameter adjustments to empiricallyidentify additional invariant dimensions (e.g., elements of recognitiontraits) that can be ignored during an image recognition process.

In embodiments, the module 160 can be programmed to, upon detecting asufficiently large change in the value of the trait 154 b (or one ormore of its elements) from the value of trait 154 a (or one or more ofits elements corresponding to those of trait 154 b), flag the keypointin the sensor data (e.g., keypoint in image data) as a candidate forfurther analysis. Having flagged one or more of these keypoints, themodule 160 can be programmed to execute a routine based on this observedvariance (dx/dp, and higher-order derivatives) and explore the spacearound this keypoint (such as via additional variance analysis withcorresponding keypoints for the new keypoint in the other images).

In other aspects of some embodiments, the trait element variancecriteria may identify a low variance among the corresponding elements inthe recognition traits across the first and second training data sets130 a and 130 b, where the low variance operates as a function of a lowvariance threshold. In addition, the trait element variance criteriacould further identify a high variance among the corresponding elementsin the recognition traits across the first and second training sets 130a and 130 b, where the high variance operates as a function of a highvariance threshold.

Module 160 is also configured to store mapping 162 in a memory 170.Memory 170 can comprise a non-transitory electronic storage medium, suchas a database, hard drive, flash drive, random access memory (RAM), orany other device suitable for electronically storing data.

Mapping 162 can include a dimensionality reduction of the elements. Inaddition, mapping 162 can represent an invariant property of one of theelements of a recognition trait with respect to adjusted environmentalparameters (e.g., one or more of parameters 103 b) or adjustedenvironmental attributes (e.g., one or more of the attributes 104 b ofparameters 103 b). Alternatively, or in addition to the invariantproperty, mapping 162 can also represent a variant property of one ofthe recognition trait elements with respect to adjusted environmentalparameters.

In some embodiments, mapping 162 can comprise a non-linear mapping fromthe plurality of elements of first recognition trait 154 a and secondrecognition trait 154 b to a plurality of elements in a newmulti-dimensional space. For example, when first recognition trait 154 aand second recognition trait 154 b are corresponding SIFT descriptors,each having 128 dimensions, the new multi-dimensional space couldcomprise a corresponding SIFT descriptor having only 30 dimensions, the30 dimensions being the most relevant of the original 128 dimensionswith respect to an adjusted environmental parameter or attribute. Asanother example, mapping 162 could comprise a non-linear mapping thattransforms features into a new multi-dimensional space such that linearclassification algorithms can be applied with the same level of efficacyas non-linear methods, per the techniques described in “EfficientAdditive Kernels via Explicit Feature Maps” published by Vedaldi et al.,in IEEE June 2011. In other aspects, such a non-linear mapping could beprovided as an optional pre-processing step for an object categorizationengine (e.g., engine 620 in FIG. 6).

In yet other aspects, mapping 162 could comprise a linear mapping fromthe plurality of elements of the first and second recognition traits 154a and 154 b to the plurality of elements in a new invariant space. Moresimply, mapping 162 could comprise a look-up table.

In another aspect of some embodiments, mapping 162 can include aninferred state of a real-world environment based on the new recognitiontrait (e.g., second recognition trait 156). Viewed from anotherperspective, mapping 162 can be used to infer an environmental state orcondition based on observed traits (e.g., descriptors, etc.). Forexample, when system 100 is deployed in a mobile smart phone, system 100can infer one or more aspects of the environmental conditions, such aslighting conditions, view point, camera information, etc. A context canbe inferred based on the measured environmental parameters. Based on thecontext, a corresponding spill tree can be selected fitting the contextsuch that the look-up is performed faster than if the entirety of thespill tree were traversed sequentially.

FIG. 6 shows a sensor data processing system 600. System 600 is similarto system 100 except that system 600 additionally includes a traitvocabulary engine 610 (and associated memory 171) and an objectcategorization engine 620 (with associated known-object database 172).

Engine 610 is configured to generate a trait vocabulary as a function ofdescriptor clusters derived from data set 130 a and/or data set 130 b.Engine 610 is also configured to save the trait vocabulary on a memory171. A trait vocabulary can be considered to be a collection of relevantdescriptors, organized into cells and indexed according to identifiers.Suitable techniques for generating the trait vocabulary are discussed inApplicant's U.S. application Ser. No. 14/622,621, entitled “GlobalVisual Vocabulary, Systems and Methods” and incorporated by referenceherein in its entirety. The trait vocabulary can include globalvocabularies for specific domains or an overarching universal vocabularyacross domains. Thus, it should be noted that the global vocabulary fora particular domain might change whereas the universal vocabulary islikely to remain unchanged, even if new training data is added. Thus,the words in the trait vocabulary for a specific object (e.g., thefigurine) might change but the global vocabulary would not.

The trait vocabulary can include a corpus of vocabulary atoms or “words”representing recognition traits (e.g., the trait vocabulary can be avocabulary of image descriptors). Trait recognition module 152 can usethe trait vocabulary to derive recognition trait 154 a and/orrecognition trait 154 b, in which the elements of traits 154 a and/or154 b comprise atoms of the vocabulary. In addition, one or moreelements of a recognition trait may also comprise adistribution/histogram of vocabulary elements occurring in input data(e.g., data set 130 a and/or data set 130 b). In this manner, firstrecognition trait 154 a and second recognition trait 154 b will beassociated with at least one of the vocabulary atoms or “words”.

In addition, mapping module 160 could be further configured to define anew vocabulary when identifying the mapping, such that recognitiontraits relative to the new vocabulary vary minimally between the twotraining sets.

In some embodiments, the corpus of vocabulary atoms can include at leastone cluster shape trait. For example, the cluster is a group of atomsthat represent elements of a recognition trait (e.g., dimensions of aSIFT descriptor) that fall on a manifold (e.g., a surface of a 3Dshape). The cluster of vocabulary atoms can include numerous subsets ofclusters that fall on multiple manifolds that define subsets ofrecognition traits. In embodiments, the cluster shape trait can be adistribution fit to the cluster.

System 600 also differs from system 100 in that it includes an objectcategorization engine 620. Object categorization engine 620 isconfigured to classify object 101 as a type of object based on at leastone of recognition trait 154 a and recognition trait 154 b. Engine 620can be further configured to store the recognition trait 154 a orrecognition trait 154 b with the type of object in a known objectdatabase 172. Database 172 includes a library of known objects andobject recognition traits. Database 172 also includes a trait-to-objectmapping or correspondence that can be used to train a discriminative orgenerative classifier (e.g., Random Forests, Support Vector Machines(SVM), Neural Nets, Boosting, etc.). The classifier can then be used inplace of database 172 to determine object identity once at least onerecognition trait is extracted from an object (e.g., first recognitiontrait 154 a).

It should be noted that FIGS. 1 and 6 are merely conceptualrepresentations of system 100 and system 600, respectively, and do notnecessarily imply any particular hardware architecture or configuration.For example, those of ordinary skill in the art will appreciate thatengine 610 in FIG. 6 could comprise separate hardware and software thanengine 150. Alternatively, engine 610 and engine 150 could shareresources and engine 610 could even comprise a module within engine 150.In addition, memory 170 and memory 171 could exist on a single device,or could alternatively be distributed together across multiple devices.Various system architectures are possible for system 100 and system 600,which may include distributed processing, LAN-to-WAN networking, virtualdatabases, cloud computing, cloud storage, and many otherconfigurations. The particular architecture of system 100 and 600 shouldnot be limited to any particular configuration unless specificallystated in the claims or otherwise specifically noted.

It is contemplated that, except for a physical object 101 being sensed,the system 100 (and also system 600) can be incorporated into a singledevice such as a smartphone or other computing device that includes asensor 108 such as a camera. As the environmental parameters 103 (andassociated attributes 104) dictating an environmental state 102 caninclude sensor and other device parameters, the controlled environment110 can be one whereby only parameters (and attributes) associated withthe device are those capable of being controlled and modified. In thisexample, there is a limited amount of control over a particularenvironment.

In another example, the controlled sensing environment 110 can be acompletely controlled environment with respect to the sensor 108, suchas a studio whereby all of the lighting parameters, positioning ofobjects 101 being imaged, appearance of objects 101 being imaged,background imagery (e.g., green screen, completely white or blackbackground, etc.), camera settings, duration of sensor capture, durationof session, and other environmental parameters 103 (both the presence ofthe environmental parameters 103 themselves within the environment andalso the attributes 104 of those parameters 103) can be tightlycontrolled and modified.

The controlled environment 110 can be considered to include multipledimensions (e.g., the different environmental parameters 103). As such,at an instant in which an image is captured by an image sensor 108 havecorresponding attribute-value pairs that describe the environment 110(as discussed above, the corresponding pair is the parameter103-attribute 104 pair that define and describe the instantenvironmental state 102). Typically, these parameters are consideredorthogonal to each other in that a modification of one attribute 104associated with a parameter 103 does not result in a change in anattribute 104 of another parameter 103. For example, in the illustrativeexample discussed above a change in lighting does not change theorientation of the object 101. Nevertheless, depending on the type ofdescriptor used, more than one dimension can be considered correlated inaffecting the resulting value of the descriptor (the effect on thedescriptor is based on the nature of the particular descriptor). Forexample, changing two environmental dimensions (i.e. attributes 104 ofenvironmental parameters 103) might affect a SIFT descriptor differentlythan a DAISY descriptor. In embodiments, the mapping module 160 can beprogrammed to employ latent variable models (such as factor analysis,latent Dirichlet allocation (LDA), probabilistic latent semanticanalysis (pLSA), Gaussian process latent variable models, etc.) toidentify these correlations for each descriptor type and then map howthe changes differ between the different object recognition algorithmsand how they behave under various changes in environmental conditions.

As used in the description herein and throughout the claims that follow,the meaning of “a,” “an,” and “the” includes plural reference unless thecontext clearly dictates otherwise. Also, as used in the descriptionherein, the meaning of “in” includes “in” and “on” unless the contextclearly dictates otherwise.

The recitation of ranges of values herein is merely intended to serve asa shorthand method of referring individually to each separate valuefalling within the range. Unless otherwise indicated herein, eachindividual value is incorporated into the specification as if it wereindividually recited herein. All methods described herein can beperformed in any suitable order unless otherwise indicated herein orotherwise clearly contradicted by context. The use of any and allexamples, or exemplary language (e.g. “such as”) provided with respectto certain embodiments herein is intended merely to better illuminatethe invention and does not pose a limitation on the scope of theinvention otherwise claimed. No language in the specification should beconstrued as indicating any non-claimed element essential to thepractice of the invention.

Groupings of alternative elements or embodiments of the inventiondisclosed herein are not to be construed as limitations. Each groupmember can be referred to and claimed individually or in any combinationwith other members of the group or other elements found herein. One ormore members of a group can be included in, or deleted from, a group forreasons of convenience and/or patentability. When any such inclusion ordeletion occurs, the specification is herein deemed to contain the groupas modified thus fulfilling the written description of all Markushgroups used in the appended claims.

As used herein, and unless the context dictates otherwise, the term“coupled to” is intended to include both direct coupling (in which twoelements that are coupled to each other contact each other) and indirectcoupling (in which at least one additional element is located betweenthe two elements). Therefore, the terms “coupled to” and “coupled with”are used synonymously.

It should be apparent to those skilled in the art that many moremodifications besides those already described are possible withoutdeparting from the inventive concepts herein. The inventive subjectmatter, therefore, is not to be restricted except in the spirit of theappended claims. Moreover, in interpreting both the specification andthe claims, all terms should be interpreted in the broadest possiblemanner consistent with the context. In particular, the terms “comprises”and “comprising” should be interpreted as referring to elements,components, or steps in a non-exclusive manner, indicating that thereferenced elements, components, or steps may be present, or utilized,or combined with other elements, components, or steps that are notexpressly referenced. Where the specification claims refers to at leastone of something selected from the group consisting of A, B, C . . . andN, the text should be interpreted as requiring only one element from thegroup, not A plus N, or B plus N, etc.

1-31. (canceled)
 32. A sensor data processing system comprising: amemory; and one or more image processing computers in communication withthe memory, the one or more image processing computers configured to:obtain a first training data set representative of at least one objectrelated to at least one person under a defined environmental statewithin a controlled environment, wherein the controlled environmentcomprises environmental parameters that correspond to environmentalattributes; generate a trait vocabulary as a function of descriptorsderived from at least the first training data set; derive a firstrecognition trait comprising a first plurality of elements from thefirst training data set according to a trait extraction algorithm,wherein the first recognition trait is derived using the traitvocabulary; obtain a second training data set representative of the atleast one object related to at least one person under a secondenvironmental state within the controlled environment, wherein thesecond environmental state is created by adjusting at least oneenvironmental parameter corresponding to an environmental attribute;derive a second recognition trait comprising a second plurality ofelements from the second training data set according to the traitextraction algorithm; and identify a mapping that maps a plurality ofelements of the first recognition trait and the second recognition traitin a new representation space, wherein the mapping of the plurality ofelements in the new representation space satisfies trait elementvariance criteria among corresponding elements in traits across thefirst training data set and the second training data set.
 33. The systemof claim 32, further comprising storing at least one of the traitvocabulary and the mapping in the memory.
 34. The system of claim 32,wherein the second recognition trait has a correspondence to the firstrecognition trait.
 35. The system of claim 32, wherein the secondrecognition trait is derived using the trait vocabulary.
 36. The systemof claim 32, wherein the trait extraction algorithm comprises an imageprocessing algorithm.
 37. The system of claim 36, wherein the imageprocessing algorithm comprises at least one of a SIFT, FAST, FREAK,BRIEF, ORB, BRISK, GLOH, SURF, vSLAM, SLAM, BURST, and DAISY imageprocessing algorithm.
 38. The system of claim 32, wherein the traitextraction algorithm comprises one or more classification algorithms andobject recognition algorithms.
 39. The system of claim 32, wherein thetrait extraction algorithm comprises at least one edge-based recognitiontechnique.
 40. The system of claim 32 wherein the first recognitiontrait comprises a descriptor.
 41. The system of claim 40, wherein thedescriptor comprises an image descriptor.
 42. The system of claim 41,wherein the first plurality of elements comprises dimensions of thedescriptor.
 43. The system of claim 32, wherein the first recognitiontrait comprises a cluster of descriptors.
 44. The system of claim 43,wherein the cluster of descriptors comprises a constellation ofdescriptors.
 45. The system of claim 44, wherein the constellation ofdescriptors is within a descriptor space.
 46. The system of claim 44,wherein the constellation of descriptors is with respect tothree-dimensional space.
 47. The system of claim 43, wherein the firstplurality of elements comprises at least one of a descriptor, adescriptor location, and a cluster of descriptors.
 48. The system ofclaim 32, wherein the trait vocabulary comprises a corpus of vocabularyatoms representing traits.
 49. The system of claim 48, wherein at leastone of the first recognition trait and the second recognition trait areassociated with at least one of the vocabulary atoms.
 50. The system ofclaim 48, wherein one or more elements of the first recognition trait orsecond recognition trait comprise a distribution or histogram ofvocabulary atoms.
 51. The system of claim 48, wherein the corpus ofvocabulary atoms representing traits comprises at least one clustershape trait.
 52. The system of claim 32, further comprising one or moreobject categorization computers configured to classify the at least oneobject related to at least one person as a type of object related tomedical information based on at least one of the first recognition traitand the second recognition trait.
 53. The system of claim 52, whereinthe one or more object categorization computers are further configuredto store the at least one of the first recognition trait and the secondrecognition trait with the type of object related to medical informationin a known object database.
 54. The system of claim 52, wherein themedical information comprises biometric data.
 55. The system of claim32, wherein at least one of the first training data set and the secondtraining data set is related to medical imaging.
 56. The system of claim32, wherein the mapping represents an invariant property as arecognition property with respect to the at least one environmentalparameter adjusted as exhibited by at least some of the first pluralityof elements.
 57. The system of claim 32, wherein the mapping representsa variant property as a recognition property with respect to the atleast one environmental parameter adjusted as exhibited by at least someof the first plurality of elements.
 58. The system of claim 32, whereinthe mapping comprises a dimensionality reduction of the plurality ofelements.
 59. The system of claim 32, wherein the mapping comprises anon-linear mapping from the first plurality of elements and the secondplurality of elements to a third plurality of elements in a newmulti-dimensional space.
 60. The system of claim 32, wherein the mappingcomprises a look-up table.
 61. The system of claim 32, wherein themapping comprises a linear mapping from the first plurality of elementsand the second plurality of elements to a third plurality of elements ina new invariant space.
 62. The system of claim 32, wherein the mappingcomprises an inferred state of a real-world environment based on thesecond recognition trait.
 63. The system of claim 32, wherein adjustingat least one environmental parameter comprises adjusting at least one ofa wireless signal, a lighting property, a camera property, a sensorproperty, an error rate, a gravity field, a shape, a distance, afield-of-view, a scale, a duration or length of time, a sampling oranalysis frequency, a distortion of time via slowing down or speeding upsensor data playback, and an orientation.
 64. The system of claim 32,wherein the defined environmental state comprises an expectedenvironmental state external to the controlled environment.
 65. Thesystem of claim 32, wherein the trait element variance criteria identifya low variance among the corresponding elements in the traits across thefirst training set and the second training set, the low varianceoperating as a function of a low variance threshold.
 66. The system ofclaim 32, wherein the trait element variance criteria identify a highvariance among the corresponding elements in the traits across the firsttraining set and the second training set, the high variance operating asa function of a high variance threshold.
 67. The system of claim 32,wherein the trait element variance criteria relate to a function of avariance with respect to the first plurality of elements.